Handwritten word recognition using lottery ticket hypothesis based pruned CNN model: a new benchmark on CMATERdb2.1.2

Handwritten word recognition, a classical pattern recognition problem, converts a word image into its machine editable form. Mainly two basic approaches are followed to solve this problem, one is segmentation-based and the other is holistic. A number of research attempts have shown that the holistic approach performs better than its counterpart when the lexicon is predefined, fixed and small in size. Relying on this, initial benchmark recognition accuracy on CMATERdb2.1.2, a publicly available database consists of handwritten city names in Bangla, was reported following a holistic word recognition protocol. In the present work, we have followed the same trend to recognize the word samples of the said database and set a new benchmark recognition accuracy. A sparse convolutional neural network (CNN)-based model which is a low-cost trainable model has been developed for this. We have relied on a recently proposed hypothesis, known as lottery ticket hypothesis for pruning the layers of CNN model methodically, and derived a low-resource model having much less number of training parameters. This model competently surpasses the previously reported recognition accuracy on the said database by a significant margin with an axed training cost.